Overview of outcome of variables

Outcome by county

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
    
demo_plot = demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>% 
    mutate(county_name = fct_reorder(county_name, prevalence)) %>% 
    ggplot(aes(x = reorder(county_name, prevalence), y = prevalence, color = county_name)) +
    geom_point(alpha = 1) +
    theme(
      legend.position = "none"
    ) +
    labs(x = "County",
         y = "Prevalence",
         title = "Prevalence Across County") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    scale_y_continuous(labels = point)

ggplotly(demo_plot, width = 800, height = 500)
point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
    
    demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>% 
    mutate(county_name = fct_reorder(county_name, prevalence)) %>%
    ggplot(aes(x = prevalence, fill = county_name)) +
    geom_histogram(alpha = 0.7) +
    labs(x = "Prevalence",
         y = "Count") +
    theme(
      legend.position = "bottom"
    ) + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point) +
          scale_x_continuous(labels = point)

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)

demo_plot_1 = demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rate = total_deaths / total_cases) %>% 
    mutate(county_name = fct_reorder(county_name, death_rate)) %>% 
    ggplot(aes(x = county_name, y = death_rate, color = county_name)) +
    geom_point(alpha = 1) +
    theme(
      legend.position = "none"
    ) +
    labs(x = "County",
         y = "Death Rate",
         title = "Death Rate Across County") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    scale_y_continuous(labels = point)

ggplotly(demo_plot_1, width = 800, height = 500)
point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
    
 demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rate = total_deaths / total_cases) %>% 
    mutate(county_name = fct_reorder(county_name, death_rate)) %>%
    ggplot(aes(x = death_rate, fill = county_name)) +
    geom_histogram(alpha = 0.7) +
    labs(x = "Death Rate",
         y = "Count") +
    theme(
      legend.position = "bottom"
    ) + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point) +
          scale_x_continuous(labels = point)

Worst counties for each COVID outcome

demo_worst_1 =
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>%
    select(county_name, prevalence) %>% 
    arrange(desc(prevalence)) %>% 
    head(10)

demo_worst_2 = 
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rates = total_deaths/total_cases) %>%
    select(county_name, death_rates) %>% 
    arrange(desc(death_rates)) %>% 
    head(10)

cbind(demo_worst_1, demo_worst_2) %>% 
    knitr::kable(digits = 4,
                 caption = "Worst counties for each COVID outcome",
                 col.names = c("County for Prevalence", "Prevelence", "County for Death Rate", "Death Rate"))
Worst counties for each COVID outcome
County for Prevalence Prevelence County for Death Rate Death Rate
Kings 0.3864 Siskiyou 0.0176
Imperial 0.3483 Shasta 0.0168
Lassen 0.3356 Sierra 0.0162
Los Angeles 0.3249 Calaveras 0.0156
San Bernardino 0.3065 Tehama 0.0156
Tuolumne 0.3034 Trinity 0.0147
Riverside 0.2853 Imperial 0.0144
San Diego 0.2785 Inyo 0.0132
Madera 0.2780 Tuolumne 0.0130
Del Norte 0.2722 Stanislaus 0.0128
demo_worst_pre_plot =
    demo_worst_1 %>%
    mutate(county_name = fct_reorder(county_name, prevalence)) %>%
    ggplot(aes(x = reorder(county_name, prevalence, decreasing = T), y = prevalence, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
    labs(x = '',
         y = "Prevalence",
         title = "Worst Prevalence") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_worst_dea_plot =
    demo_worst_2 %>%
    mutate(county_name = fct_reorder(county_name, death_rates)) %>%
    ggplot(aes(x = reorder(county_name, death_rates, decreasing = T), y = death_rates, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
    labs(x = '',
         y = "Death Rate",
         title = "Worst Death Rate") + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_worst_pre_plot + demo_worst_dea_plot

Best counties for each COVID outcome

demo_best_1 =
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>%
    select(county_name, prevalence) %>% 
    arrange(prevalence) %>% 
    head(10)

demo_best_2 = 
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rates = total_deaths/total_cases) %>%
    select(county_name, death_rates) %>% 
    arrange(death_rates) %>% 
    head(10)

cbind(demo_best_1, demo_best_2) %>% 
    knitr::kable(digits = 4,
                 caption = "Best counties for each COVID outcome",
                 col.names = c("County for Prevalence", "Prevelence", "County for Death Rate", "Death Rate"))
Best counties for each COVID outcome
County for Prevalence Prevelence County for Death Rate Death Rate
Modoc 0.0945 Alpine 0.0000
Sierra 0.0989 Mono 0.0025
Trinity 0.1067 Plumas 0.0035
Siskiyou 0.1225 Santa Cruz 0.0042
Alpine 0.1235 San Mateo 0.0043
Humboldt 0.1667 Solano 0.0044
Colusa 0.1700 Sonoma 0.0049
El Dorado 0.1712 Napa 0.0049
Mariposa 0.1774 Colusa 0.0057
Marin 0.1775 Alameda 0.0058
demo_best_pre_plot =
    demo_best_1 %>% 
    mutate(county_name = fct_reorder(county_name, prevalence)) %>%
    ggplot(aes(x = reorder(county_name, prevalence, decreasing = F), y = prevalence, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
    labs(x = '',
         y = "Prevalence",
         title = "Best Prevalence") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_best_dea_plot =
    demo_best_2 %>% 
    mutate(county_name = fct_reorder(county_name, death_rates)) %>% 
    ggplot(aes(x = reorder(county_name, death_rates, decreasing = F), y = death_rates, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
     labs(x = '',
         y = "Death Rate",
         title = "Best Death Rate") + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_best_pre_plot + demo_best_dea_plot

Association between outcomes across county

Age vs Total Cases

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
grid.arrange(
 
    age %>% 
    ggplot(aes(x = age_group, y = total_cases, fill = age_group)) +
    geom_bar(stat = "identity", width = 0.5) +
    scale_fill_viridis(discrete = TRUE) +
    theme(
      legend.position = "none"
    ) +
    xlab("Age") +
    ylab("Total cases") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point),
    

    age %>% 
    ggplot(aes(x = total_cases, fill = age_group)) +
    geom_histogram(aes(y = ..density..), alpha = 0.5, bins = 50) +
    geom_density(alpha = 0.3, aes(color = age_group)) +
    scale_color_viridis(discrete = TRUE) +
    labs(x = "Total cases",
         y = "Density") +
    theme(legend.position = "right") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
    scale_x_continuous(labels = point) +
    scale_y_continuous(labels = point),
    
    
    age %>%
    ggplot(aes(x = age_group, y = total_cases, fill = age_group)) +
    geom_boxplot(alpha = 0.5) +
    geom_hline(yintercept = median(age$total_cases, na.rm = T), color = "red", size = 0.4, lty = "dashed") +
    scale_fill_viridis(discrete = TRUE) +
    theme(
      legend.position = "none"
    ) +
    xlab("Age") +
    ylab("Total cases") +
        theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point),


layout_matrix = rbind(c(1, 1, 1, 2, 2, 2, 2),
                      c(1, 1, 1, 2, 2, 2, 2),
                      c(1, 1, 1, 2, 2, 2, 2),
                      c(3, 3, 3, 2, 2, 2, 2),
                      c(3, 3, 3, 2, 2, 2, 2)
))

# Highest Total cases
age_17 = 
  age %>% 
  filter(age_group == "0-17") %>% 
  arrange(date) %>% 
  mutate(lag = lag(total_cases)) %>% 
  mutate(growth_perc = (((total_cases - lag) / total_cases)) * 100)


age_49 =
  age %>% 
  filter(age_group == "18-49") %>% 
  arrange(date) %>% 
  mutate(lag = lag(total_cases)) %>% 
  mutate(growth_perc = (((total_cases - lag) / total_cases)) * 100) 

age_64 = 
  age %>% 
  filter(age_group == "50-64") %>% 
  arrange(date) %>% 
  mutate(lag = lag(total_cases)) %>% 
  mutate(growth_perc = (((total_cases - lag) / total_cases)) * 100)

age_65 =
  age %>% 
  filter(age_group == "65+") %>% 
  arrange(date) %>% 
  mutate(lag = lag(total_cases)) %>% 
  mutate(growth_perc = (((total_cases - lag) / total_cases)) * 100)

age_all = 
    rbind(age_17, age_49, age_64, age_65) %>% 
    arrange(desc(growth_perc)) %>% 
    select(date, age_group, total_cases, growth_perc)

head(age_all) %>% 
    knitr::kable(
    caption = "Highest total cases growth rate by age",
    col.names = c("Date", "Age", "Total cases", "Growth rate"),
    digits = 2
  )
Highest total cases growth rate by age
Date Age Total cases Growth rate
2020-04-29 0-17 1398 9.66
2020-04-27 0-17 1190 8.82
2020-04-23 0-17 936 8.65
2020-05-05 0-17 1937 8.47
2022-01-09 0-17 945336 8.29
2022-01-16 0-17 1158294 7.62
age_all_low = 
    rbind(age_17, age_49, age_64, age_65) %>% 
    arrange(growth_perc) %>% 
    select(date, age_group, total_cases, growth_perc)

head(age_all_low) %>% 
    knitr::kable(
    caption = "Lowest total cases growth rate by age",
    col.names = c("Date", "Age", "Total cases", "Growth rate"),
    digits = 2
  )
Lowest total cases growth rate by age
Date Age Total cases Growth rate
2021-06-29 65+ 391708 -0.17
2021-06-29 50-64 703990 -0.13
2021-06-29 18-49 2127853 -0.11
2021-06-29 0-17 484599 -0.10
2021-04-23 65+ 384595 -0.01
2020-12-23 0-17 234174 0.00

[Text] Note: the red line of boxplot is median

Gender vs Total Cases

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
grid.arrange(
 
    gender %>% 
    ggplot(aes(x = gender, y = total_cases, fill = gender)) +
    geom_bar(stat = "identity", width = 0.5) +
    scale_fill_viridis(discrete = TRUE) +
    theme(
      legend.position = "none"
    ) +
    xlab("Gender") +
    ylab("Total cases") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
    scale_y_continuous(labels = point),

    gender %>% 
    ggplot(aes(x = total_cases, fill = gender)) +
    geom_histogram(aes(y = ..density..), alpha = 0.5, bins = 30) +
    geom_density(alpha = 0.3, aes(color = gender)) +
    scale_color_viridis(discrete = TRUE) +
    labs(x = "Total cases",
         y = "Density") +
    theme(legend.position = "right") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
    scale_x_continuous(labels = point) +
    scale_y_continuous(labels = point) +
    scale_fill_viridis(discrete = TRUE),
    
    
    gender %>%
    ggplot(aes(x = gender, y = total_cases, fill = gender)) +
    geom_boxplot(alpha = 0.5) +
    geom_hline(yintercept = median(gender$total_cases, na.rm = T), color = "red", size = 0.4, lty = "dashed") +
    scale_fill_viridis(discrete = TRUE) +
    theme(
      legend.position = "none"
    ) +
    xlab("Gender") +
    ylab("Total cases") +
        theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point),


layout_matrix = rbind(c(1, 1, 1, 2, 2, 2, 2),
                      c(1, 1, 1, 2, 2, 2, 2),
                      c(1, 1, 1, 2, 2, 2, 2),
                      c(3, 3, 3, 2, 2, 2, 2),
                      c(3, 3, 3, 2, 2, 2, 2)
))

gender_M =
  gender %>% 
  filter(gender == "Male") %>% 
  arrange(date) %>% 
  mutate(lag = lag(total_cases)) %>% 
  mutate(growth_perc = (((total_cases - lag) / total_cases)) * 100)

gender_F =
  gender %>% 
  filter(gender == "Female") %>% 
  arrange(date) %>% 
  mutate(lag = lag(total_cases)) %>% 
  mutate(growth_perc = (((total_cases - lag) / total_cases)) * 100)

gender_all =
    rbind(gender_M, gender_F) %>%
    arrange(desc(growth_perc)) %>% 
    select(date, gender, total_cases, growth_perc)

head(gender_all) %>% 
    knitr::kable(
    caption = "Highest total cases growth rate by gender",
    col.names = c("Date", "Gender", "Total cases", "Growth rate"),
    digits = 2
  )
Highest total cases growth rate by gender
Date Gender Total cases Growth rate
2020-04-29 Male 24372 5.44
2022-01-09 Female 3034425 5.31
2020-04-23 Female 19394 5.15
2020-04-24 Female 20395 4.91
2022-01-09 Male 2813232 4.89
2022-01-16 Female 3445681 4.80
gender_all_1 =
    rbind(gender_M, gender_F) %>%
    arrange(growth_perc) %>% 
    select(date, gender, total_cases, growth_perc)
head(gender_all_1) %>% 
    knitr::kable(
    caption = "Lowest total cases growth rate by gender",
    col.names = c("Date", "Gender", "Total cases", "Growth rate"),
    digits = 2
  )
Lowest total cases growth rate by gender
Date Gender Total cases Growth rate
2021-06-29 Male 1774418 -0.12
2021-06-29 Female 1884983 -0.11
2020-12-23 Male 945758 0.00
2020-12-30 Male 1064781 0.00
2020-12-23 Female 993649 0.00
2020-12-30 Female 1121071 0.00

Race vs Total Cases

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
grid.arrange(
 
    race %>%
    mutate(race_group = recode(race_group, "American Indian or Alaska Native" = "Indian/Alaska",
           "Native Hawaiian and other Pacific Islander" = "Hawaiian/Islander")) %>% 
    mutate(race_group = fct_reorder(race_group, total_cases)) %>%
    ggplot(aes(x = race_group, y = total_cases, fill = race_group)) +
    geom_bar(stat = "identity", width = 0.5) +
    scale_fill_viridis(discrete = TRUE) +
    theme(
      legend.position = "none"
    ) +
    xlab("Race") +
    ylab("Total cases") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
    scale_y_continuous(labels = point) +
    theme(axis.title.x = element_blank(),
         axis.text.x = element_blank(),
         axis.ticks.x = element_blank()),
    

    race %>% 
    mutate(race_group = recode(race_group, "American Indian or Alaska Native" = "Indian/Alaska",
           "Native Hawaiian and other Pacific Islander" = "Hawaiian/Islander")) %>% 
    mutate(race_group = fct_reorder(race_group, total_cases)) %>%
    ggplot(aes(x = total_cases, fill = race_group)) +
    geom_histogram(aes(y = ..density..), alpha = 0.5, bins = 30) +
    geom_density(alpha = 0.1, aes(color = race_group)) +
    scale_color_viridis(discrete = TRUE) +
    labs(x = "Total cases",
         y = "Density") +
    theme(legend.position = "right") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) + 
    xlim(0, 2.5e6) +   
    ylim(0, 1.5e-5),
   
    


race %>%
    mutate(race_group = fct_reorder(race_group, total_cases)) %>% 
    mutate(race_group = recode(race_group, "American Indian or Alaska Native" = "Indian/Alaska",
           "Native Hawaiian and other Pacific Islander" = "Hawaiian/Islander")) %>% 
    ggplot(aes(x = race_group, y = total_cases, fill = race_group)) +
    geom_boxplot(alpha = 0.5) +
    geom_hline(yintercept = median(race$total_cases, na.rm = T), color = "red", size = 0.4, lty = "dashed") +
    ylim(0, 30000) +
    scale_fill_viridis(discrete = TRUE) +
    theme(
      legend.position = "none"
    ) +
    xlab("Race") +
    ylab("Total cases") +
        theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)),
 


layout_matrix = rbind(c(1, 1, 1, 2, 2, 2, 2),
                      c(1, 1, 1, 2, 2, 2, 2),
                      c(1, 1, 1, 2, 2, 2, 2),
                      c(3, 3, 3, 2, 2, 2, 2),
                      c(3, 3, 3, 2, 2, 2, 2)
))

(table for race and demo have not finished yet)

[Text]

Death Rate

Age vs Death Rate

age %>% 
    mutate(date = factor(date)) %>%
    mutate(text_label = str_c("Date: ", date, 
                              "\n Age: ", age_group,
                              "\n Death(%): ", percent_deaths)) %>%
    plot_ly(y = ~percent_deaths, 
          x = ~date, 
          color = ~age_group, 
          width = 950,
          height = 300, 
          type = "scatter",
          mode = "markers",
          marker = list(size = 3),
          colors = "inferno",
          text = ~ text_label) %>%
    layout(xaxis = list(
           title = "Date",
           tickangle = 60),
           yaxis = list(
           title = "Death Rate"))
<<<<<<< HEAD
=======
>>>>>>> 9e60a84f59421de4a4b9636c60eedb6db9bccc03

[Text]

Gender vs Death Rate

gender %>% 
    mutate(date = factor(date)) %>%
    mutate(text_label = str_c("Date: ", date, 
                              "\n Gender: ", gender,
                              "\n Death(%): ", percent_deaths)) %>%
    plot_ly(y = ~percent_deaths, 
          x = ~date, 
          color = ~gender, 
          width = 950,
          height = 300, 
          type = "scatter",
          mode = "markers",
          marker = list(size = 3),
          colors = "viridis",
          text = ~ text_label) %>%
    layout(xaxis = list(
           title = "Date",
           tickangle = 60),
           yaxis = list(
           title = "Death Rate"))
<<<<<<< HEAD
=======
>>>>>>> 9e60a84f59421de4a4b9636c60eedb6db9bccc03

[Text]

Race vs Death Rate

race %>% 
    mutate(date = factor(date)) %>%
    mutate(race_group = recode(race_group, "American Indian or Alaska Native" = "Indian/Alaska",
           "Native Hawaiian and other Pacific Islander" = "Hawaiian/Islander")) %>% 
    mutate(text_label = str_c("Date: ", date, 
                              "\n Race: ", race_group,
                              "\n Death(%): ", percent_deaths)) %>%
    plot_ly(y = ~percent_deaths, 
          x = ~date, 
          color = ~race_group, 
          width = 950,
          height = 300, 
          type = "scatter",
          mode = "markers",
          marker = list(size = 3),
          colors = "inferno",
          text = ~ text_label) %>%
    layout(xaxis = list(
           title = "Date",
           tickangle = 60),
           yaxis = list(
           title = "Death Rate"))
<<<<<<< HEAD

[Text]

Area vs Death Rate

demo %>% 
    mutate(percent_deaths = (cumulative_deaths / cumulative_cases) * 100) %>%
    mutate(date = factor(date)) %>%
    mutate(text_label = str_c("Date: ", date, 
                              "\n Area: ", county_name,
                              "\n Death(%): ", percent_deaths)) %>%
    plot_ly(y = ~percent_deaths, 
          x = ~date, 
          color = ~county_name , 
          width = 950,
          height = 500, 
          type = "scatter",
          mode = "markers",
          marker = list(size = 3),
          colors = "inferno",
          text = ~ text_label) %>%
    layout(xaxis = list(
           title = "Date",
           tickangle = 60),
           yaxis = list(
           title = "Death Rate",
           range = c(0, 13)))

[Text: Note: states users can use our dashboard to research this]

=======

[Text]

>>>>>>> 9e60a84f59421de4a4b9636c60eedb6db9bccc03